MSBVAR (version 0.4.0)

msbvar: Markov-switching Bayesian reduced form vector autoregression model setup and posterior mode estimation

Description

Sets up and estimates the posterior mode of a reduced form Markov-switching Bayesian vector autoregression model with a Sims-Zha prior. This is the setup and input function for the Gibbs sampler for this model.

Usage

msbvar(y, z = NULL, p, h, lambda0, lambda1, lambda3, lambda4,
       lambda5, mu5, mu6, qm,
       alpha.prior = 100 * diag(h) + matrix(2, h, h),
       prior=0, max.iter = 40)

Value

A list describing the posterior mode of the MSBVAR model and the inputs necessary for the subsequent Gibbs sampler.init.modelAn object of the class BVAR that describes the setup of the model. See szbvar for details.hregA list containing the regime-specific moment matrices, VAR coefficients, and error covariancesQThe $h \times h$ Markov transition matrix.fpThe $T \times h$ matrix of the filtered regime probabilities. First column is the first regime, etc.mInteger, the number of endogenous variables in the system.pInteger, the lag length of the VAR.hInteger, the number of regimes in the MS process.alpha.priorThe $h \times h$ matrix for the prior for the Dirichlet density for the MS process.

Details

This function estimates the posterior mode of a reduced form Bayesian Markov-switching VAR model. The MSBVAR mode is estimated using block EM algorithm where the blocks are 1) the MS state-space, 2) the BVAR regression step for each regime and 3) the transition matrix. Starting values are randomly drawn, so a random number seed should be set prior to calling the function in order to make the results replicable.

This function should NOT be used for inference, since it only finds the posterior mode of the model. This function is intended to generate starting values for the Gibbs sampling of the model. See gibbs.msbvar for further details of the Gibbs sampling.

References

Brandt, Patrick T. 2009. "Empirical, Regime-Specific Models of International, Inter-group Conflict, and Politics" Fruhwirth-Schnatter, Sylvia. 2001. "Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models". Journal of the American Statistical Association. 96(153):194--209.

Fruhwirth-Schnatter, Sylvia. 2006. Finite Mixture and Markov Switching Models. Springer Series in Statistics New York: Springer. Sims, Christopher A. and Daniel F. Waggoner and Tao Zha. 2008. "Methods for inference in large multiple-equation Markov-switching models" Journal of Econometrics 146(2):255--274. Sims, Christopher A. and Tao A. Zha. 1998. "Bayesian Methods for Dynamic Multivariate Models" International Economic Review 39(4):949-968. Sims, Christopher A. and Tao A. Zha. 2006. "Were There Regime Switches in U.S. Monetary Policy?" American Economic Review. 96(1):54--81.

See Also

gibbs.msbvar, szbvar